{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## _*H2 dissociation curve using VQE with UCCSD*_\n",
    "\n",
    "This notebook demonstrates using Qiskit Chemistry to plot graphs of the ground state energy of the Hydrogen (H2) molecule over a range of inter-atomic distances using VQE and UCCSD. It is compared to the same energies as computed by the NumPyMinimumEigensolver. `UCCSD` should be used together with `HartreeFock` initial state.\n",
    "\n",
    "This notebook has been written to use the PYQUANTE chemistry driver. See the PYQUANTE chemistry driver readme if you need to install the external PyQuante2 library that this driver requires."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing step 20 --- complete\n",
      "Distances:  [0.5   0.525 0.55  0.575 0.6   0.625 0.65  0.675 0.7   0.725 0.75  0.775\n",
      " 0.8   0.825 0.85  0.875 0.9   0.925 0.95  0.975 1.   ]\n",
      "Energies: [[-1.05515973 -1.0759136  -1.09262986 -1.105918   -1.11628598 -1.12416089\n",
      "  -1.12990475 -1.13382618 -1.13618942 -1.13722134 -1.13711706 -1.13604434\n",
      "  -1.13414766 -1.13155119 -1.12836187 -1.12467174 -1.12056027 -1.11609624\n",
      "  -1.11133942 -1.10634211 -1.10115032]\n",
      " [-1.05515974 -1.07591361 -1.09262987 -1.10591802 -1.11628599 -1.12416089\n",
      "  -1.12990476 -1.1338262  -1.13618944 -1.13722136 -1.13711707 -1.13604436\n",
      "  -1.13414767 -1.13155121 -1.12836188 -1.12467175 -1.12056028 -1.11609624\n",
      "  -1.11133943 -1.10634212 -1.10115034]]\n",
      "Hartree-Fock energies: [-1.04299622 -1.0630621  -1.0790507  -1.09157046 -1.10112822 -1.10814997\n",
      " -1.11299652 -1.11597525 -1.11734902 -1.11734325 -1.11615145 -1.11393966\n",
      " -1.1108504  -1.10700581 -1.10251056 -1.09745432 -1.09191405 -1.08595588\n",
      " -1.07963694 -1.07300677 -1.06610866]\n",
      "VQE num evaluations: [49. 53. 50. 50. 41. 47. 49. 47. 51. 46. 42. 57. 45. 49. 49. 50. 50. 46.\n",
      " 54. 56. 55.]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pylab\n",
    "import copy\n",
    "from qiskit import BasicAer\n",
    "from qiskit.aqua import QuantumInstance\n",
    "from qiskit.aqua.algorithms import NumPyMinimumEigensolver, VQE\n",
    "from qiskit.aqua.components.optimizers import COBYLA\n",
    "from qiskit.chemistry.components.initial_states import HartreeFock\n",
    "from qiskit.chemistry.components.variational_forms import UCCSD\n",
    "from qiskit.chemistry.drivers import PyQuanteDriver, BasisType\n",
    "from qiskit.chemistry.core import Hamiltonian, TransformationType, QubitMappingType\n",
    "\n",
    "molecule = 'H .0 .0 -{0}; H .0 .0 {0}'\n",
    "algorithms = ['VQE', 'NumPyMinimumEigensolver']\n",
    "\n",
    "start = 0.5  # Start distance\n",
    "by    = 0.5  # How much to increase distance by\n",
    "steps = 20   # Number of steps to increase by\n",
    "energies = np.empty([len(algorithms), steps+1])\n",
    "hf_energies = np.empty(steps+1)\n",
    "distances = np.empty(steps+1)\n",
    "eval_counts = np.empty(steps+1)\n",
    "\n",
    "print('Processing step __', end='')\n",
    "for i in range(steps+1):\n",
    "    print('\\b\\b{:2d}'.format(i), end='', flush=True)\n",
    "    d = start + i*by/steps \n",
    "    for j in range(len(algorithms)):\n",
    "        driver = PyQuanteDriver(atoms=molecule.format(d/2), basis=BasisType.BSTO3G)\n",
    "        qmolecule = driver.run()\n",
    "        operator =  Hamiltonian(qubit_mapping=QubitMappingType.JORDAN_WIGNER,\n",
    "                                two_qubit_reduction=False)\n",
    "        qubit_op, aux_ops = operator.run(qmolecule)\n",
    "        \n",
    "        if algorithms[j] == 'NumPyMinimumEigensolver':\n",
    "            result = NumPyMinimumEigensolver(qubit_op).run()\n",
    "        else:\n",
    "            optimizer = COBYLA(maxiter=10000)\n",
    "            initial_state = HartreeFock(operator.molecule_info['num_orbitals'],\n",
    "                                        operator.molecule_info['num_particles'],\n",
    "                                        qubit_mapping=operator._qubit_mapping,\n",
    "                                        two_qubit_reduction=operator._two_qubit_reduction)\n",
    "            var_form = UCCSD(num_orbitals=operator.molecule_info['num_orbitals'],\n",
    "                             num_particles=operator.molecule_info['num_particles'],\n",
    "                             initial_state=initial_state,\n",
    "                             qubit_mapping=operator._qubit_mapping,\n",
    "                             two_qubit_reduction=operator._two_qubit_reduction)\n",
    "            algo = VQE(qubit_op, var_form, optimizer)\n",
    "            result = algo.run(QuantumInstance(BasicAer.get_backend('statevector_simulator')))\n",
    "            eval_counts[i] = result.optimizer_evals\n",
    "        \n",
    "        result = operator.process_algorithm_result(result)\n",
    "        energies[j][i] = result.energy\n",
    "        hf_energies[i] = result.hartree_fock_energy\n",
    "\n",
    "    distances[i] = d\n",
    "print(' --- complete')\n",
    "\n",
    "print('Distances: ', distances)\n",
    "print('Energies:', energies)\n",
    "print('Hartree-Fock energies:', hf_energies)\n",
    "print('VQE num evaluations:', eval_counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(distances, hf_energies, label='Hartree-Fock')\n",
    "for j in range(len(algorithms)):\n",
    "    pylab.plot(distances, energies[j], label=algorithms[j])\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Energy')\n",
    "pylab.title('H2 Ground State Energy')\n",
    "pylab.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(distances, np.subtract(hf_energies, energies[1]), label='Hartree-Fock')\n",
    "pylab.plot(distances, np.subtract(energies[0], energies[1]), label='VQE')\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Energy')\n",
    "pylab.title('Energy difference from NumPyMinimumEigensolver')\n",
    "pylab.legend(loc='upper left');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(distances, eval_counts, '-o', color=[0.8500, 0.3250, 0.0980], label='VQE')\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Evaluations')\n",
    "pylab.title('VQE number of evaluations')\n",
    "pylab.legend(loc='upper left');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
